Prevention of colon cancer can be achieved by detecting and surgically removing the polyps from the colon wail. However, the colonoscopy procedure used for detecting the polyps is a time consuming procedure that produces great discomfort for the patient. Virtual colonoscopy is an increasingly popular alternative in which the patient's colon is inflated with air through a rectal tube and then one or two Computed Tomography (CT) scans of the abdomen are performed. A polyp detection method is used on the CT scans and the detection, results are reported to the doctor for inspection. The current polyp detection methods exhibit a relatively large numbers of false positives due to the rectal tube used to inflate the colon. Those false positives can be reduced by detecting and segmenting the rectal tube and discarding any potential positives that are close to the rectal tube.
A rectal tube detection method should be fast and have a very low false positive rate, since false positives can decrease the detection rate of the overall polyp detection system. A known method for rectal tube detection handles the appearance by template matching, which is a relatively rigid method for detection, and the shape variability by tracking 2-dimensional (2D) slices. The tracking assumes that the tube is relatively perpendicular to one of the axes, which is often not true as shown in FIG. 1. FIG. 1 illustrates that the rectal tubes 102-124 are flexible and variable shape and appearance. The method only handles two types of rectal tubes and was validated on a relatively small number of cases (i.e., 80 datasets). The method also involved a large amount of potentially time consuming morphological operations such as region growing.
Another known method for reducing false positives due to rectal tubes involves using a Massive Trained Artificial Neural Network (MTANN) to distinguish between polyps and rectal tubes which raise questions about the degree of control of the generalization power of the system. There is a need for a method for detecting flexible tubes in an object that provides a large degree of control against overfitting the data.